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CNNLab: a Novel Parallel Framework for Neural Networks using GPU and FPGA-a Practical Study with Trade-off Analysis

Maohua Zhu, Liu Liu, Chao Wang, Yuan Xie

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Tags: CNN, Computer science, CUDA, Deep learning, FPGA, MapReduce, Neural networks, nVidia, OpenCL, Tesla K40

June 22, 2016 by hgpu

Soft GPGPUs for Embedded FPGAs: An Architectural Evaluation

Kevin Andryc, Tedy Thomas, Russell Tessier

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Tags: Benchmarking, Computer science, CUDA, DSP, FPGA

June 22, 2016 by hgpu

OpenCL-Based Erasure Coding on Heterogeneous Architectures

Guoyang Chen, Huiyang Zhou, Xipeng Shen, Josh Gahm, Narayan Venkat, Skip Booth, John Marshall

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Tags: Algorithms, APU, ATI, Computer science, Error recovery, FPGA, Heterogeneous systems, nVidia, OpenCL, Tesla K40

June 14, 2016 by hgpu

Co-tuning of Software Specializers and Hardware Accelerators within a CNN Application

Sean Roberts

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Tags: ARM, CNN, Computer science, Deep learning, FPGA, Neural networks, SoC

June 7, 2016 by hgpu

Bridging the Performance-Programmability Gap for FPGAs via OpenCL: A Case Study with OpenDwarfs

Konstantinos Krommydas, Ahmed E. Helal, Anshuman Verma, Wu-Chun Feng

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Tags: Benchmarking, Computer science, FPGA, Intel Xeon Phi, OpenCL, performance portability, Tesla C2070, Tesla K20

May 30, 2016 by hgpu

Efficient High-Speed WPA2 Brute Force Attacks using Scalable Low-Cost FPGA Clustering

Markus Kammerstetter, Markus Muellner, Daniel Burian, Christian Kudera, Wolfgang Kastner

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Tags: Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 770, nVidia GRID K520, Security

May 28, 2016 by hgpu

A Foray into Efficient Mapping of Algorithms to Hardware Platforms on Heterogeneous Systems

Oren Segal, Nasibeh Nasiri, Martin Margala

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Tags: Benchmarking, Computer science, FPGA, Heterogeneous systems, nVidia, OpenCL, Tesla K40

May 17, 2016 by hgpu

Going Deeper with Embedded FPGA Platform for Convolutional Neural Network

Jiantao Qiu, Jie Wang, Song Yao, Kaiyuan Guo, Boxun Li, Erjin Zhou, Jincheng Yu, Tianqi Tang, Ningyi Xu, Sen Song, Yu Wang, Huazhong Yang

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, FPGA, Neural networks, nVidia, nVidia Jetson TK1, Tesla K40

May 7, 2016 by hgpu

Monte-Carlo Black-Scholes Implementation using OpenCL Standard

Chirag Patel, M. Srikanth, Kalyani Chetan Kumar, S. Sivanantham

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Tags: Finance, FPGA, OpenCL

April 9, 2016 by hgpu

Melia: A MapReduce Framework on OpenCL-based FPGAs

Zeke Wang, Shuhao Zhang, Bingsheng He, Wei Zhang

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Tags: Computer science, FPGA, MapReduce, OpenCL, Package

March 15, 2016 by hgpu

Deep Learning on FPGAs: Past, Present, and Future

Griffin Lacey, Graham W. Taylor, Shawki Areibi

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Tags: Artificial intelligence, Computer science, Deep learning, FPGA, Machine learning, OpenCL, Overview

February 17, 2016 by hgpu

GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths

Nachiket Kapre, Deheng Ye

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Tags: Benchmarking, Code generation, Computer science, CUDA, FPGA, nVidia, Package, Performance, Tesla K20

February 10, 2016 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

CNNLab: a Novel Parallel Framework for Neural Networks using GPU and FPGA-a Practical Study with Trade-off Analysis

Soft GPGPUs for Embedded FPGAs: An Architectural Evaluation

OpenCL-Based Erasure Coding on Heterogeneous Architectures

Co-tuning of Software Specializers and Hardware Accelerators within a CNN Application

Bridging the Performance-Programmability Gap for FPGAs via OpenCL: A Case Study with OpenDwarfs

Efficient High-Speed WPA2 Brute Force Attacks using Scalable Low-Cost FPGA Clustering

A Foray into Efficient Mapping of Algorithms to Hardware Platforms on Heterogeneous Systems

Going Deeper with Embedded FPGA Platform for Convolutional Neural Network

Monte-Carlo Black-Scholes Implementation using OpenCL Standard

Melia: A MapReduce Framework on OpenCL-based FPGAs

Deep Learning on FPGAs: Past, Present, and Future

GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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